This files contains an example of tuning a multiple models with BayesSearchCV.
import pickle
import time
import helpsk as hlp
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
import plotly.io as pio
pio.renderers.default='notebook'
with open('../X_train.pkl', 'rb') as handle:
X_train = pickle.load(handle)
with open('../y_train.pkl', 'rb') as handle:
y_train = pickle.load(handle)
hlp.pandas.numeric_summary(X_train, return_style=True)
| # of Non-Nulls | # of Nulls | % Nulls | # of Zeros | % Zeros | Mean | St Dev. | Coef of Var | Skewness | Kurtosis | Min | 10% | 25% | 50% | 75% | 90% | Max | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| duration | 760 | 40 | 5.0% | 0 | 0.0% | 21.0 | 11.7 | 0.6 | 1.0 | 0.6 | 4.0 | 9.0 | 12.0 | 18.0 | 24.0 | 36.0 | 60.0 |
| credit_amount | 800 | 0 | 0.0% | 38 | 5.0% | 3,203.9 | 2,932.3 | 0.9 | 1.9 | 3.9 | 0.0 | 753.9 | 1,300.8 | 2,236.5 | 3,951.5 | 7,394.6 | 18,424.0 |
| installment_commitment | 800 | 0 | 0.0% | 0 | 0.0% | 3.0 | 1.1 | 0.4 | -0.5 | -1.2 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 4.0 | 4.0 |
| residence_since | 800 | 0 | 0.0% | 0 | 0.0% | 2.9 | 1.1 | 0.4 | -0.3 | -1.4 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 4.0 | 4.0 |
| age | 800 | 0 | 0.0% | 0 | 0.0% | 35.6 | 11.4 | 0.3 | 1.0 | 0.7 | 19.0 | 23.0 | 27.0 | 33.0 | 42.0 | 52.0 | 75.0 |
| existing_credits | 800 | 0 | 0.0% | 0 | 0.0% | 1.4 | 0.6 | 0.4 | 1.3 | 1.6 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 4.0 |
| num_dependents | 800 | 0 | 0.0% | 0 | 0.0% | 1.1 | 0.3 | 0.3 | 2.0 | 2.1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 |
hlp.pandas.non_numeric_summary(X_train, return_style=True)
| # of Non-Nulls | # of Nulls | % Nulls | Most Freq. Value | # of Unique | % Unique | |
|---|---|---|---|---|---|---|
| checking_status | 763 | 37 | 4.6% | no checking | 4 | 0.5% |
| credit_history | 800 | 0 | 0.0% | existing paid | 5 | 0.6% |
| purpose | 800 | 0 | 0.0% | radio/tv | 10 | 1.2% |
| savings_status | 800 | 0 | 0.0% | <100 | 5 | 0.6% |
| employment | 800 | 0 | 0.0% | 1<=X<4 | 5 | 0.6% |
| personal_status | 800 | 0 | 0.0% | male single | 4 | 0.5% |
| other_parties | 800 | 0 | 0.0% | none | 3 | 0.4% |
| property_magnitude | 800 | 0 | 0.0% | car | 4 | 0.5% |
| other_payment_plans | 800 | 0 | 0.0% | none | 3 | 0.4% |
| housing | 800 | 0 | 0.0% | own | 3 | 0.4% |
| job | 800 | 0 | 0.0% | skilled | 4 | 0.5% |
| own_telephone | 800 | 0 | 0.0% | none | 2 | 0.2% |
| foreign_worker | 800 | 0 | 0.0% | yes | 2 | 0.2% |
y_train[0:10]
array([1, 1, 0, 1, 0, 1, 0, 1, 1, 0])
np.unique(y_train, return_counts=True)
(array([0, 1]), array([559, 241]))
np.unique(y_train, return_counts=True)[1] / np.sum(np.unique(y_train, return_counts=True)[1])
array([0.69875, 0.30125])
search_space = hlp.sklearn_search.BayesianSearchSpace(
data=X_train,
iterations=75,
random_state=42,
)
# pip install scikit-optimize
from skopt import BayesSearchCV
from skopt.space import Real, Categorical, Integer
from sklearn.model_selection import RepeatedKFold
bayes_search = BayesSearchCV(
estimator=search_space.pipeline(),
search_spaces=search_space.search_spaces(),
cv=RepeatedKFold(n_splits=5, n_repeats=2, random_state=42), # 5 fold 2 repeat CV
scoring='roc_auc',
refit=False, # required if passing in multiple scorers
return_train_score=False,
n_jobs=-1,
verbose=1,
random_state=42,
)
start_time = time.time()
bayes_search.fit(X_train, y_train)
elapsed_time = time.time() - start_time
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/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( /Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/sklearn/svm/_base.py:1199: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn(
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits
/Users/shanekercheval/opt/anaconda3/envs/python-examples/lib/python3.9/site-packages/skopt/optimizer/optimizer.py:449: UserWarning: The objective has been evaluated at this point before.
Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits Fitting 10 folds for each of 1 candidates, totalling 10 fits 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print(f"Elapsed time to run BayesSearchCV: {elapsed_time:.3f} seconds; {elapsed_time / 60:.1f} minutes")
Elapsed time to run BayesSearchCV: 865.445 seconds; 14.4 minutes
print(bayes_search.best_score_)
0.7784870815859175
print(bayes_search.best_params_)
OrderedDict([('model', RandomForestClassifier(n_estimators=500, random_state=42)), ('model__criterion', 'gini'), ('model__max_depth', 59), ('model__max_features', 0.06158460572508429), ('model__max_samples', 0.5), ('model__min_samples_leaf', 1), ('model__min_samples_split', 50), ('prep__non_numeric__encoder__transformer', OneHotEncoder(handle_unknown='ignore')), ('prep__numeric__imputer__transformer', SimpleImputer(strategy='most_frequent')), ('prep__numeric__scaler__transformer', None)])
results = hlp.sklearn_eval.MLExperimentResults.from_sklearn_search_cv(
searcher=bayes_search,
higher_score_is_better=True,
description='BayesSearchCV using ClassifierSearchSpace',
parameter_name_mappings=search_space.param_name_mappings()
)
results.to_yaml_file(yaml_file_name = 'Run 1 - Multi-model - BayesSearchCV.yaml')
results = hlp.sklearn_eval.MLExperimentResults.from_yaml_file(yaml_file_name = 'Run 1 - Multi-model - BayesSearchCV.yaml')
results.best_score
0.7784870815859175
results.best_params
{'model': 'RandomForestClassifier()',
'max_features': 0.05711695701069725,
'max_depth': 100,
'min_samples_split': 50,
'min_samples_leaf': 1,
'max_samples': 0.5,
'criterion': 'gini',
'imputer': "SimpleImputer(strategy='most_frequent')",
'scaler': 'None',
'encoder': 'OneHotEncoder()'}
results.to_formatted_dataframe(return_style=True,
include_rank=True,
num_rows=1000)
| rank | roc_auc Mean | roc_auc 95CI.LO | roc_auc 95CI.HI | model | C | max_features | max_depth | min_samples_split | min_samples_leaf | max_samples | criterion | learning_rate | n_estimators | min_child_weight | subsample | colsample_bytree | colsample_bylevel | reg_alpha | reg_lambda | imputer | scaler | encoder |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.057 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 2 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.064 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 3 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.058 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 4 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.062 | 59.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 5 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.049 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 6 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.060 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 7 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.058 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 8 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.055 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 9 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.062 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 10 | 0.778 | 0.756 | 0.801 | RandomForestClassifier() | <NA> | 0.062 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 11 | 0.776 | 0.752 | 0.801 | RandomForestClassifier() | <NA> | 0.073 | 36.000 | 50.000 | 1.000 | 0.780 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 12 | 0.775 | 0.751 | 0.800 | RandomForestClassifier() | <NA> | 0.067 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 13 | 0.775 | 0.751 | 0.800 | RandomForestClassifier() | <NA> | 0.065 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 14 | 0.775 | 0.751 | 0.800 | RandomForestClassifier() | <NA> | 0.071 | 85.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 15 | 0.775 | 0.751 | 0.800 | RandomForestClassifier() | <NA> | 0.066 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 16 | 0.775 | 0.750 | 0.801 | RandomForestClassifier() | <NA> | 0.086 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 17 | 0.775 | 0.751 | 0.799 | RandomForestClassifier() | <NA> | 0.083 | 57.000 | 33.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 18 | 0.774 | 0.751 | 0.798 | RandomForestClassifier() | <NA> | 0.010 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 19 | 0.774 | 0.751 | 0.798 | RandomForestClassifier() | <NA> | 0.077 | 9.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 20 | 0.774 | 0.751 | 0.797 | RandomForestClassifier() | <NA> | 0.055 | 28.000 | 14.000 | 3.000 | 0.781 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 21 | 0.774 | 0.751 | 0.797 | RandomForestClassifier() | <NA> | 0.010 | 82.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 22 | 0.774 | 0.751 | 0.797 | RandomForestClassifier() | <NA> | 0.010 | 44.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 23 | 0.774 | 0.751 | 0.797 | RandomForestClassifier() | <NA> | 0.010 | 43.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 24 | 0.773 | 0.750 | 0.796 | RandomForestClassifier() | <NA> | 0.010 | 43.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 25 | 0.773 | 0.750 | 0.795 | RandomForestClassifier() | <NA> | 0.010 | 18.000 | 50.000 | 1.000 | 0.661 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 26 | 0.772 | 0.751 | 0.794 | RandomForestClassifier() | <NA> | 0.010 | 32.000 | 50.000 | 1.000 | 0.976 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 27 | 0.772 | 0.749 | 0.795 | RandomForestClassifier() | <NA> | 0.134 | 55.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 28 | 0.772 | 0.746 | 0.798 | XGBClassifier() | <NA> | <NA> | 15.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 388.000 | 1.000 | 0.500 | 0.500 | 0.500 | 0.011 | 1.668 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 29 | 0.772 | 0.749 | 0.794 | RandomForestClassifier() | <NA> | 0.010 | 61.000 | 50.000 | 1.000 | 0.756 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 30 | 0.771 | 0.744 | 0.798 | RandomForestClassifier() | <NA> | 0.010 | 1.000 | 50.000 | 1.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 31 | 0.771 | 0.746 | 0.797 | RandomForestClassifier() | <NA> | 0.010 | 1.000 | 28.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 32 | 0.771 | 0.748 | 0.794 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.034 | 100.000 | 1.000 | 0.605 | 0.500 | 0.500 | 0.285 | 4.000 | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 33 | 0.771 | 0.745 | 0.796 | XGBClassifier() | <NA> | <NA> | 28.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 201.000 | 1.000 | 0.500 | 0.500 | 0.500 | 0.092 | 1.020 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 34 | 0.770 | 0.750 | 0.791 | RandomForestClassifier() | <NA> | 0.132 | 35.000 | 50.000 | 1.000 | 0.920 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 35 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 36 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.059 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 37 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 38 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 39 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 40 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 41 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 42 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 43 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 44 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 45 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 46 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 47 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 48 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 49 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 50 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 51 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 52 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 53 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 54 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 55 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 56 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.060 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 57 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 58 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 59 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.060 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 60 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.060 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 61 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 62 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.060 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 63 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.060 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 64 | 0.770 | 0.751 | 0.789 | RandomForestClassifier() | <NA> | 0.068 | 92.000 | 46.000 | 3.000 | 0.614 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 65 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.061 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 66 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.062 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 67 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.062 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 68 | 0.770 | 0.751 | 0.790 | LogisticRegression() | 0.062 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 69 | 0.770 | 0.750 | 0.790 | LogisticRegression() | 0.062 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 70 | 0.770 | 0.744 | 0.796 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 5.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 71 | 0.770 | 0.744 | 0.796 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 5.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 72 | 0.770 | 0.755 | 0.785 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 2.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 73 | 0.770 | 0.750 | 0.789 | LogisticRegression() | 0.058 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | OneHotEncoder() |
| 74 | 0.769 | 0.740 | 0.799 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 5.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 75 | 0.769 | 0.748 | 0.791 | RandomForestClassifier() | <NA> | 0.130 | 100.000 | 2.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 76 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.008 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 77 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.008 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 78 | 0.769 | 0.741 | 0.797 | ExtraTreesClassifier() | <NA> | 0.021 | 97.000 | 10.000 | 7.000 | 0.910 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 79 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.008 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 80 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.008 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 81 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.009 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 82 | 0.769 | 0.739 | 0.799 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 9.000 | 0.834 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 83 | 0.769 | 0.743 | 0.795 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 6.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 84 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.008 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 85 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.008 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 86 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.009 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 87 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.009 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 88 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.010 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 89 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.010 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 90 | 0.769 | 0.748 | 0.790 | LinearSVC(random_state=42) | 0.009 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 91 | 0.769 | 0.748 | 0.789 | LinearSVC(random_state=42) | 0.011 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 92 | 0.769 | 0.748 | 0.789 | LinearSVC(random_state=42) | 0.011 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 93 | 0.769 | 0.745 | 0.792 | RandomForestClassifier() | <NA> | 0.083 | 15.000 | 47.000 | 9.000 | 0.871 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 94 | 0.768 | 0.748 | 0.789 | LinearSVC(random_state=42) | 0.013 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 95 | 0.768 | 0.748 | 0.789 | LinearSVC(random_state=42) | 0.011 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 96 | 0.768 | 0.748 | 0.789 | LinearSVC(random_state=42) | 0.011 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 97 | 0.768 | 0.748 | 0.789 | LinearSVC(random_state=42) | 0.011 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 98 | 0.768 | 0.748 | 0.789 | LinearSVC(random_state=42) | 0.011 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 99 | 0.768 | 0.744 | 0.793 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 3.000 | 0.758 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 100 | 0.768 | 0.738 | 0.799 | ExtraTreesClassifier() | <NA> | 0.012 | 88.000 | 14.000 | 12.000 | 0.937 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 101 | 0.768 | 0.747 | 0.789 | LinearSVC(random_state=42) | 0.014 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 102 | 0.768 | 0.747 | 0.789 | LinearSVC(random_state=42) | 0.011 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 103 | 0.768 | 0.739 | 0.796 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 50.000 | 8.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 104 | 0.768 | 0.741 | 0.795 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 228.000 | 1.000 | 0.737 | 0.585 | 1.000 | 0.000 | 1.817 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 105 | 0.768 | 0.742 | 0.794 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 4.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 106 | 0.768 | 0.747 | 0.789 | LinearSVC(random_state=42) | 0.015 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 107 | 0.768 | 0.747 | 0.788 | LinearSVC(random_state=42) | 0.016 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 108 | 0.768 | 0.747 | 0.788 | LinearSVC(random_state=42) | 0.016 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 109 | 0.767 | 0.744 | 0.791 | XGBClassifier() | <NA> | <NA> | 15.000 | <NA> | <NA> | <NA> | <NA> | 0.038 | 100.000 | 2.000 | 0.500 | 0.500 | 0.808 | 1.000 | 3.491 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 110 | 0.767 | 0.739 | 0.795 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 50.000 | 5.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 111 | 0.767 | 0.746 | 0.788 | LinearSVC(random_state=42) | 0.017 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 112 | 0.767 | 0.747 | 0.787 | RandomForestClassifier() | <NA> | 0.035 | 21.000 | 47.000 | 8.000 | 0.997 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 113 | 0.767 | 0.746 | 0.788 | LinearSVC(random_state=42) | 0.018 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 114 | 0.767 | 0.746 | 0.788 | LinearSVC(random_state=42) | 0.017 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 115 | 0.767 | 0.746 | 0.788 | LinearSVC(random_state=42) | 0.018 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 116 | 0.767 | 0.746 | 0.788 | LinearSVC(random_state=42) | 0.018 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 117 | 0.767 | 0.740 | 0.793 | ExtraTreesClassifier() | <NA> | 0.084 | 92.000 | 13.000 | 6.000 | 0.535 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 118 | 0.767 | 0.746 | 0.788 | LinearSVC(random_state=42) | 0.018 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 119 | 0.767 | 0.736 | 0.797 | ExtraTreesClassifier() | <NA> | 0.010 | 51.000 | 50.000 | 9.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 120 | 0.767 | 0.746 | 0.788 | LinearSVC(random_state=42) | 0.019 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 121 | 0.767 | 0.746 | 0.787 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.041 | 100.000 | 1.000 | 0.571 | 0.500 | 0.642 | 1.000 | 1.000 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 122 | 0.766 | 0.741 | 0.792 | ExtraTreesClassifier() | <NA> | 0.015 | 81.000 | 44.000 | 4.000 | 0.614 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 123 | 0.766 | 0.743 | 0.790 | XGBClassifier() | <NA> | <NA> | 9.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 100.000 | 1.000 | 0.708 | 0.500 | 0.500 | 0.015 | 2.079 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 124 | 0.766 | 0.745 | 0.787 | LogisticRegression() | 0.016 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 125 | 0.766 | 0.743 | 0.788 | RandomForestClassifier() | <NA> | 0.307 | 100.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 126 | 0.766 | 0.749 | 0.782 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 33.000 | 1.000 | 0.856 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 127 | 0.766 | 0.746 | 0.785 | LinearSVC(random_state=42) | 0.002 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | OneHotEncoder() |
| 128 | 0.766 | 0.739 | 0.792 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 2,000.000 | 2.000 | 0.757 | 0.500 | 0.534 | 1.000 | 1.000 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 129 | 0.766 | 0.742 | 0.789 | RandomForestClassifier() | <NA> | 0.032 | 22.000 | 2.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 130 | 0.766 | 0.743 | 0.788 | RandomForestClassifier() | <NA> | 0.047 | 57.000 | 49.000 | 22.000 | 0.501 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 131 | 0.765 | 0.732 | 0.799 | ExtraTreesClassifier() | <NA> | 0.010 | 1.000 | 2.000 | 6.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 132 | 0.765 | 0.739 | 0.790 | XGBClassifier() | <NA> | <NA> | 16.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 373.000 | 2.000 | 0.500 | 0.500 | 0.651 | 1.000 | 1.099 | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 133 | 0.765 | 0.742 | 0.787 | RandomForestClassifier() | <NA> | 0.592 | 96.000 | 33.000 | 1.000 | 0.926 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 134 | 0.765 | 0.744 | 0.785 | LogisticRegression() | 0.281 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 135 | 0.764 | 0.738 | 0.791 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 50.000 | 1.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 136 | 0.764 | 0.734 | 0.795 | LogisticRegression() | 0.059 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 137 | 0.764 | 0.731 | 0.797 | ExtraTreesClassifier() | <NA> | 0.010 | 1.000 | 2.000 | 1.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 138 | 0.764 | 0.737 | 0.791 | LinearSVC(random_state=42) | 0.032 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 139 | 0.764 | 0.735 | 0.793 | LogisticRegression() | 0.132 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | OneHotEncoder() |
| 140 | 0.764 | 0.734 | 0.794 | LinearSVC(random_state=42) | 0.009 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 141 | 0.764 | 0.735 | 0.793 | LinearSVC(random_state=42) | 0.015 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 142 | 0.764 | 0.742 | 0.786 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.029 | 136.000 | 4.000 | 0.902 | 0.500 | 0.605 | 1.000 | 1.425 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 143 | 0.764 | 0.741 | 0.786 | RandomForestClassifier() | <NA> | 0.034 | 3.000 | 11.000 | 1.000 | 0.596 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 144 | 0.764 | 0.740 | 0.787 | RandomForestClassifier() | <NA> | 0.242 | 100.000 | 2.000 | 1.000 | 0.707 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 145 | 0.764 | 0.742 | 0.785 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 20.000 | 1.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 146 | 0.764 | 0.747 | 0.780 | RandomForestClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 1.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 147 | 0.763 | 0.738 | 0.789 | RandomForestClassifier() | <NA> | 0.136 | 1.000 | 13.000 | 1.000 | 0.729 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 148 | 0.763 | 0.740 | 0.787 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.167 | 100.000 | 1.000 | 0.519 | 1.000 | 0.506 | 1.000 | 1.379 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 149 | 0.763 | 0.743 | 0.783 | XGBClassifier() | <NA> | <NA> | 15.000 | <NA> | <NA> | <NA> | <NA> | 0.037 | 166.000 | 1.000 | 0.500 | 0.500 | 0.500 | 0.306 | 1.526 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 150 | 0.763 | 0.738 | 0.788 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 151 | 0.762 | 0.736 | 0.789 | RandomForestClassifier() | <NA> | 0.238 | 60.000 | 5.000 | 1.000 | 0.964 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 152 | 0.762 | 0.738 | 0.787 | RandomForestClassifier() | <NA> | 0.022 | 3.000 | 40.000 | 43.000 | 0.810 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 153 | 0.762 | 0.741 | 0.783 | LinearSVC(random_state=42) | 0.042 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 154 | 0.762 | 0.733 | 0.791 | ExtraTreesClassifier() | <NA> | 0.247 | 100.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 155 | 0.762 | 0.745 | 0.780 | ExtraTreesClassifier() | <NA> | 0.033 | 100.000 | 9.000 | 5.000 | 0.626 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 156 | 0.762 | 0.740 | 0.784 | RandomForestClassifier() | <NA> | 0.950 | 100.000 | 50.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 157 | 0.762 | 0.737 | 0.787 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 450.000 | 1.000 | 0.919 | 0.500 | 0.500 | 0.002 | 4.000 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 158 | 0.762 | 0.740 | 0.784 | RandomForestClassifier() | <NA> | 0.052 | 53.000 | 27.000 | 34.000 | 0.973 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 159 | 0.762 | 0.742 | 0.781 | ExtraTreesClassifier() | <NA> | 0.010 | 76.000 | 43.000 | 1.000 | 0.551 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 160 | 0.761 | 0.737 | 0.786 | ExtraTreesClassifier() | <NA> | 0.835 | 27.000 | 14.000 | 1.000 | 0.568 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 161 | 0.761 | 0.736 | 0.787 | ExtraTreesClassifier() | <NA> | 0.010 | 34.000 | 45.000 | 3.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 162 | 0.761 | 0.743 | 0.780 | ExtraTreesClassifier() | <NA> | 0.071 | 86.000 | 35.000 | 1.000 | 0.550 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 163 | 0.761 | 0.734 | 0.788 | ExtraTreesClassifier() | <NA> | 0.081 | 100.000 | 2.000 | 12.000 | 0.908 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 164 | 0.761 | 0.738 | 0.785 | RandomForestClassifier() | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 165 | 0.761 | 0.738 | 0.785 | RandomForestClassifier() | <NA> | 0.085 | 47.000 | 8.000 | 39.000 | 0.939 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 166 | 0.761 | 0.738 | 0.784 | XGBClassifier() | <NA> | <NA> | 36.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 100.000 | 1.000 | 0.865 | 0.500 | 0.500 | 1.000 | 3.161 | SimpleImputer() | None | OneHotEncoder() |
| 167 | 0.761 | 0.735 | 0.787 | RandomForestClassifier() | <NA> | 0.950 | 100.000 | 19.000 | 1.000 | 0.799 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 168 | 0.761 | 0.736 | 0.786 | LogisticRegression() | 0.566 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 169 | 0.761 | 0.735 | 0.787 | RandomForestClassifier() | <NA> | 0.712 | 100.000 | 2.000 | 1.000 | 0.500 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 170 | 0.761 | 0.737 | 0.784 | ExtraTreesClassifier() | <NA> | 0.405 | 100.000 | 29.000 | 1.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 171 | 0.760 | 0.733 | 0.787 | RandomForestClassifier() | <NA> | 0.949 | 16.000 | 32.000 | 1.000 | 0.533 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 172 | 0.760 | 0.743 | 0.777 | ExtraTreesClassifier() | <NA> | 0.010 | 18.000 | 2.000 | 1.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 173 | 0.760 | 0.737 | 0.783 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 2,000.000 | 3.000 | 0.511 | 0.500 | 0.746 | 1.000 | 1.000 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 174 | 0.760 | 0.735 | 0.785 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.110 | 100.000 | 1.000 | 0.679 | 0.500 | 0.500 | 0.000 | 1.605 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 175 | 0.760 | 0.740 | 0.780 | XGBClassifier() | <NA> | <NA> | 3.000 | <NA> | <NA> | <NA> | <NA> | 0.096 | 100.000 | 1.000 | 0.500 | 1.000 | 0.642 | 1.000 | 1.407 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 176 | 0.760 | 0.734 | 0.786 | ExtraTreesClassifier() | <NA> | 0.538 | 8.000 | 24.000 | 5.000 | 0.893 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 177 | 0.760 | 0.734 | 0.785 | RandomForestClassifier() | <NA> | 0.426 | 91.000 | 49.000 | 2.000 | 0.506 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 178 | 0.760 | 0.738 | 0.782 | ExtraTreesClassifier() | <NA> | 0.284 | 100.000 | 50.000 | 8.000 | 0.963 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 179 | 0.760 | 0.740 | 0.779 | ExtraTreesClassifier() | <NA> | 0.193 | 100.000 | 50.000 | 8.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 180 | 0.759 | 0.730 | 0.789 | LogisticRegression() | 0.009 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 181 | 0.759 | 0.737 | 0.781 | RandomForestClassifier() | <NA> | 0.070 | 54.000 | 11.000 | 44.000 | 0.904 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 182 | 0.759 | 0.737 | 0.781 | LogisticRegression() | 0.008 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 183 | 0.759 | 0.736 | 0.781 | LinearSVC(random_state=42) | 0.121 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | OneHotEncoder() |
| 184 | 0.759 | 0.739 | 0.779 | LogisticRegression() | 0.762 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 185 | 0.759 | 0.729 | 0.788 | LinearSVC(random_state=42) | 0.002 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 186 | 0.758 | 0.736 | 0.781 | LinearSVC(random_state=42) | 0.001 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 187 | 0.758 | 0.740 | 0.776 | XGBClassifier() | <NA> | <NA> | 10.000 | <NA> | <NA> | <NA> | <NA> | 0.076 | 100.000 | 4.000 | 0.500 | 0.500 | 0.500 | 0.008 | 3.328 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 188 | 0.758 | 0.741 | 0.775 | ExtraTreesClassifier() | <NA> | 0.033 | 62.000 | 2.000 | 9.000 | 0.814 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 189 | 0.758 | 0.736 | 0.779 | ExtraTreesClassifier() | <NA> | 0.335 | 100.000 | 2.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 190 | 0.758 | 0.731 | 0.784 | ExtraTreesClassifier() | <NA> | 0.950 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 191 | 0.757 | 0.730 | 0.784 | ExtraTreesClassifier() | <NA> | 0.172 | 100.000 | 42.000 | 10.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 192 | 0.757 | 0.738 | 0.777 | LogisticRegression() | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 193 | 0.757 | 0.735 | 0.779 | LogisticRegression() | 1.401 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 194 | 0.757 | 0.733 | 0.782 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 1,368.000 | 4.000 | 0.500 | 0.548 | 0.925 | 0.000 | 1.350 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 195 | 0.757 | 0.728 | 0.786 | LogisticRegression() | 0.004 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 196 | 0.757 | 0.728 | 0.786 | LinearSVC(random_state=42) | 0.002 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 197 | 0.757 | 0.739 | 0.774 | RandomForestClassifier() | <NA> | 0.010 | 100.000 | 50.000 | 50.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 198 | 0.757 | 0.731 | 0.782 | RandomForestClassifier() | <NA> | 0.537 | 13.000 | 50.000 | 1.000 | 0.710 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 199 | 0.756 | 0.727 | 0.786 | ExtraTreesClassifier() | <NA> | 0.489 | 96.000 | 13.000 | 1.000 | 0.536 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 200 | 0.756 | 0.734 | 0.778 | RandomForestClassifier() | <NA> | 0.010 | 100.000 | 2.000 | 1.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 201 | 0.756 | 0.737 | 0.776 | LinearSVC(random_state=42) | 0.169 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 202 | 0.756 | 0.728 | 0.784 | RandomForestClassifier() | <NA> | 0.904 | 92.000 | 44.000 | 1.000 | 0.553 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 203 | 0.756 | 0.727 | 0.784 | LogisticRegression() | 0.001 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | OneHotEncoder() |
| 204 | 0.756 | 0.731 | 0.780 | ExtraTreesClassifier() | <NA> | 0.860 | 95.000 | 30.000 | 11.000 | 0.831 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 205 | 0.756 | 0.732 | 0.779 | ExtraTreesClassifier() | <NA> | 0.877 | 90.000 | 5.000 | 7.000 | 0.823 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 206 | 0.755 | 0.727 | 0.784 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 207 | 0.755 | 0.729 | 0.782 | ExtraTreesClassifier() | <NA> | 0.257 | 15.000 | 22.000 | 11.000 | 0.510 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 208 | 0.755 | 0.732 | 0.779 | ExtraTreesClassifier() | <NA> | 0.478 | 32.000 | 41.000 | 9.000 | 0.841 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 209 | 0.755 | 0.727 | 0.784 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 210 | 0.755 | 0.727 | 0.784 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 211 | 0.755 | 0.727 | 0.784 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 212 | 0.755 | 0.732 | 0.778 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 100.000 | 3.000 | 0.500 | 0.642 | 0.933 | 1.000 | 2.451 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 213 | 0.755 | 0.727 | 0.783 | ExtraTreesClassifier() | <NA> | 0.589 | 100.000 | 50.000 | 1.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 214 | 0.755 | 0.727 | 0.784 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 215 | 0.755 | 0.726 | 0.784 | ExtraTreesClassifier() | <NA> | 0.010 | 100.000 | 27.000 | 17.000 | 0.612 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 216 | 0.755 | 0.733 | 0.777 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.054 | 183.000 | 5.000 | 0.847 | 0.732 | 0.657 | 0.990 | 2.392 | SimpleImputer() | None | OneHotEncoder() |
| 217 | 0.755 | 0.732 | 0.777 | ExtraTreesClassifier() | <NA> | 0.924 | 11.000 | 11.000 | 4.000 | 0.592 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 218 | 0.754 | 0.729 | 0.780 | ExtraTreesClassifier() | <NA> | 0.950 | 100.000 | 2.000 | 15.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 219 | 0.754 | 0.729 | 0.779 | ExtraTreesClassifier() | <NA> | 0.871 | 8.000 | 8.000 | 15.000 | 0.923 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 220 | 0.754 | 0.731 | 0.778 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.242 | 100.000 | 1.000 | 0.838 | 1.000 | 0.500 | 1.000 | 2.956 | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 221 | 0.754 | 0.733 | 0.775 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.426 | 100.000 | 1.000 | 0.500 | 0.500 | 0.990 | 0.189 | 2.705 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 222 | 0.754 | 0.731 | 0.776 | RandomForestClassifier() | <NA> | 0.080 | 49.000 | 49.000 | 21.000 | 0.596 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 223 | 0.754 | 0.732 | 0.776 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 1,698.000 | 5.000 | 0.720 | 0.707 | 0.542 | 0.121 | 1.839 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 224 | 0.754 | 0.730 | 0.778 | ExtraTreesClassifier() | <NA> | 0.864 | 100.000 | 50.000 | 1.000 | 0.500 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 225 | 0.753 | 0.730 | 0.776 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.042 | 208.000 | 7.000 | 0.679 | 0.500 | 0.595 | 1.000 | 1.749 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 226 | 0.753 | 0.731 | 0.775 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.181 | 100.000 | 1.000 | 0.899 | 1.000 | 1.000 | 0.009 | 3.300 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 227 | 0.753 | 0.728 | 0.778 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.101 | 100.000 | 1.000 | 0.798 | 0.500 | 0.500 | 0.053 | 1.470 | SimpleImputer() | None | OneHotEncoder() |
| 228 | 0.753 | 0.727 | 0.779 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 295.000 | 2.000 | 0.543 | 0.914 | 0.500 | 0.000 | 1.459 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 229 | 0.753 | 0.728 | 0.777 | ExtraTreesClassifier() | <NA> | 0.937 | 52.000 | 47.000 | 1.000 | 0.997 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 230 | 0.753 | 0.727 | 0.779 | RandomForestClassifier() | <NA> | 0.587 | 98.000 | 14.000 | 9.000 | 0.511 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 231 | 0.753 | 0.727 | 0.778 | XGBClassifier() | <NA> | <NA> | 5.000 | <NA> | <NA> | <NA> | <NA> | 0.004 | 200.000 | 6.000 | 0.867 | 0.625 | 0.614 | 0.221 | 2.698 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 232 | 0.753 | 0.729 | 0.776 | XGBClassifier() | <NA> | <NA> | 5.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 1,547.000 | 3.000 | 0.976 | 0.629 | 0.993 | 0.000 | 1.000 | SimpleImputer() | None | OneHotEncoder() |
| 233 | 0.752 | 0.733 | 0.771 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.260 | 100.000 | 1.000 | 1.000 | 1.000 | 0.862 | 0.000 | 4.000 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 234 | 0.752 | 0.727 | 0.776 | RandomForestClassifier() | <NA> | 0.035 | 65.000 | 12.000 | 47.000 | 0.932 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 235 | 0.752 | 0.727 | 0.776 | ExtraTreesClassifier() | <NA> | 0.617 | 98.000 | 28.000 | 15.000 | 0.670 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 236 | 0.752 | 0.725 | 0.779 | ExtraTreesClassifier() | <NA> | 0.041 | 43.000 | 40.000 | 28.000 | 0.708 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 237 | 0.752 | 0.734 | 0.769 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.037 | 100.000 | 4.000 | 1.000 | 0.879 | 0.500 | 0.016 | 1.396 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 238 | 0.751 | 0.732 | 0.770 | LogisticRegression() | 3.854 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 239 | 0.751 | 0.729 | 0.773 | RandomForestClassifier() | <NA> | 0.319 | 100.000 | 5.000 | 26.000 | 0.950 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 240 | 0.751 | 0.721 | 0.780 | ExtraTreesClassifier() | <NA> | 0.020 | 32.000 | 19.000 | 21.000 | 0.553 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 241 | 0.751 | 0.733 | 0.768 | LinearSVC(random_state=42) | 0.805 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 242 | 0.751 | 0.733 | 0.768 | LinearSVC(random_state=42) | 0.766 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 243 | 0.751 | 0.725 | 0.776 | RandomForestClassifier() | <NA> | 0.663 | 93.000 | 7.000 | 2.000 | 0.982 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 244 | 0.750 | 0.729 | 0.771 | RandomForestClassifier() | <NA> | 0.208 | 15.000 | 50.000 | 50.000 | 0.536 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 245 | 0.750 | 0.724 | 0.776 | ExtraTreesClassifier() | <NA> | 0.638 | 100.000 | 2.000 | 1.000 | 0.616 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 246 | 0.750 | 0.732 | 0.767 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.500 | 100.000 | 1.000 | 0.528 | 0.730 | 0.781 | 0.007 | 1.149 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 247 | 0.750 | 0.724 | 0.776 | XGBClassifier() | <NA> | <NA> | 2.000 | <NA> | <NA> | <NA> | <NA> | 0.055 | 111.000 | 8.000 | 0.568 | 0.878 | 0.827 | 1.000 | 1.747 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 248 | 0.750 | 0.729 | 0.770 | ExtraTreesClassifier() | <NA> | 0.412 | 63.000 | 17.000 | 15.000 | 0.734 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 249 | 0.750 | 0.733 | 0.767 | LinearSVC(random_state=42) | 0.877 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 250 | 0.750 | 0.730 | 0.770 | XGBClassifier() | <NA> | <NA> | 4.000 | <NA> | <NA> | <NA> | <NA> | 0.165 | 100.000 | 1.000 | 1.000 | 0.500 | 0.908 | 1.000 | 3.785 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 251 | 0.749 | 0.732 | 0.767 | LinearSVC(random_state=42) | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 252 | 0.749 | 0.732 | 0.767 | LinearSVC(random_state=42) | 1.136 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 253 | 0.749 | 0.722 | 0.776 | ExtraTreesClassifier() | <NA> | 0.919 | 3.000 | 29.000 | 1.000 | 0.642 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 254 | 0.748 | 0.719 | 0.777 | ExtraTreesClassifier() | <NA> | 0.011 | 47.000 | 26.000 | 30.000 | 0.828 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 255 | 0.748 | 0.724 | 0.772 | RandomForestClassifier() | <NA> | 0.333 | 52.000 | 2.000 | 28.000 | 0.656 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 256 | 0.748 | 0.731 | 0.764 | LogisticRegression() | 8.368 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 257 | 0.747 | 0.730 | 0.765 | LinearSVC(random_state=42) | 1.950 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | OneHotEncoder() |
| 258 | 0.747 | 0.727 | 0.768 | ExtraTreesClassifier() | <NA> | 0.474 | 100.000 | 2.000 | 23.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 259 | 0.747 | 0.726 | 0.768 | ExtraTreesClassifier() | <NA> | 0.405 | 15.000 | 30.000 | 22.000 | 0.792 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 260 | 0.747 | 0.718 | 0.777 | ExtraTreesClassifier() | <NA> | 0.614 | 19.000 | 2.000 | 1.000 | 0.657 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 261 | 0.746 | 0.722 | 0.770 | ExtraTreesClassifier() | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 262 | 0.746 | 0.723 | 0.769 | RandomForestClassifier() | <NA> | 0.026 | 3.000 | 32.000 | 49.000 | 0.580 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 263 | 0.746 | 0.722 | 0.770 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 1,499.000 | 2.000 | 1.000 | 0.704 | 1.000 | 0.001 | 1.157 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 264 | 0.746 | 0.724 | 0.767 | RandomForestClassifier() | <NA> | 0.064 | 99.000 | 40.000 | 50.000 | 0.507 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 265 | 0.745 | 0.719 | 0.772 | XGBClassifier() | <NA> | <NA> | 8.000 | <NA> | <NA> | <NA> | <NA> | 0.062 | 188.000 | 12.000 | 0.735 | 0.852 | 0.798 | 0.108 | 3.989 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 266 | 0.745 | 0.726 | 0.763 | LinearSVC(random_state=42) | 9.270 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 267 | 0.745 | 0.728 | 0.762 | LogisticRegression() | 23.327 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | OneHotEncoder() |
| 268 | 0.745 | 0.722 | 0.767 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.024 | 118.000 | 5.000 | 0.989 | 0.890 | 1.000 | 0.000 | 2.163 | SimpleImputer() | None | OneHotEncoder() |
| 269 | 0.745 | 0.724 | 0.765 | ExtraTreesClassifier() | <NA> | 0.576 | 83.000 | 32.000 | 29.000 | 0.637 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 270 | 0.745 | 0.720 | 0.769 | RandomForestClassifier() | <NA> | 0.324 | 6.000 | 3.000 | 48.000 | 0.964 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 271 | 0.745 | 0.721 | 0.768 | XGBClassifier() | <NA> | <NA> | 36.000 | <NA> | <NA> | <NA> | <NA> | 0.009 | 2,000.000 | 1.000 | 0.902 | 0.942 | 0.991 | 1.000 | 4.000 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 272 | 0.744 | 0.721 | 0.766 | RandomForestClassifier() | <NA> | 0.798 | 64.000 | 12.000 | 26.000 | 0.834 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 273 | 0.743 | 0.722 | 0.765 | ExtraTreesClassifier() | <NA> | 0.602 | 1.000 | 2.000 | 1.000 | 0.583 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 274 | 0.743 | 0.715 | 0.771 | LogisticRegression() | 0.002 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 275 | 0.743 | 0.725 | 0.760 | LogisticRegression() | 98.353 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 276 | 0.742 | 0.728 | 0.757 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.327 | 228.000 | 1.000 | 0.503 | 0.820 | 1.000 | 0.000 | 1.258 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 277 | 0.742 | 0.725 | 0.759 | LogisticRegression() | 88.500 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 278 | 0.741 | 0.718 | 0.765 | RandomForestClassifier() | <NA> | 0.467 | 66.000 | 36.000 | 31.000 | 0.845 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 279 | 0.741 | 0.719 | 0.763 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.102 | 271.000 | 1.000 | 0.917 | 0.661 | 0.500 | 1.000 | 2.473 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 280 | 0.741 | 0.722 | 0.760 | ExtraTreesClassifier() | <NA> | 0.031 | 6.000 | 26.000 | 20.000 | 0.951 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 281 | 0.741 | 0.717 | 0.765 | XGBClassifier() | <NA> | <NA> | 46.000 | <NA> | <NA> | <NA> | <NA> | 0.115 | 180.000 | 1.000 | 0.620 | 0.777 | 0.854 | 0.006 | 3.537 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 282 | 0.740 | 0.715 | 0.766 | XGBClassifier() | <NA> | <NA> | 2.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 100.000 | 3.000 | 0.669 | 0.774 | 0.798 | 0.004 | 4.000 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 283 | 0.740 | 0.716 | 0.764 | ExtraTreesClassifier() | <NA> | 0.927 | 85.000 | 5.000 | 23.000 | 0.557 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 284 | 0.740 | 0.716 | 0.764 | ExtraTreesClassifier() | <NA> | 0.872 | 13.000 | 47.000 | 28.000 | 0.808 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 285 | 0.737 | 0.716 | 0.759 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.040 | 1,697.000 | 1.000 | 1.000 | 0.500 | 0.500 | 0.001 | 1.095 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 286 | 0.737 | 0.721 | 0.753 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.383 | 422.000 | 1.000 | 0.756 | 0.891 | 0.500 | 0.021 | 2.643 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 287 | 0.737 | 0.710 | 0.764 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 288 | 0.737 | 0.708 | 0.765 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 289 | 0.737 | 0.717 | 0.756 | RandomForestClassifier() | <NA> | 0.665 | 80.000 | 50.000 | 50.000 | 0.888 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 290 | 0.736 | 0.708 | 0.765 | ExtraTreesClassifier() | <NA> | 0.240 | 1.000 | 2.000 | 1.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 291 | 0.736 | 0.716 | 0.756 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 100.000 | 13.000 | 0.500 | 0.500 | 0.500 | 0.190 | 2.803 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 292 | 0.735 | 0.714 | 0.757 | ExtraTreesClassifier() | <NA> | 0.930 | 8.000 | 41.000 | 31.000 | 0.720 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 293 | 0.735 | 0.712 | 0.758 | ExtraTreesClassifier() | <NA> | 0.735 | 63.000 | 19.000 | 36.000 | 0.628 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 294 | 0.735 | 0.713 | 0.757 | ExtraTreesClassifier() | <NA> | 0.778 | 1.000 | 2.000 | 10.000 | 0.887 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 295 | 0.735 | 0.713 | 0.757 | XGBClassifier() | <NA> | <NA> | 2.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 539.000 | 5.000 | 0.513 | 0.881 | 0.997 | 0.000 | 1.000 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 296 | 0.735 | 0.712 | 0.757 | RandomForestClassifier() | <NA> | 0.827 | 39.000 | 19.000 | 32.000 | 0.503 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 297 | 0.735 | 0.709 | 0.760 | ExtraTreesClassifier() | <NA> | 0.222 | 1.000 | 2.000 | 23.000 | 0.765 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 298 | 0.734 | 0.711 | 0.758 | XGBClassifier() | <NA> | <NA> | 12.000 | <NA> | <NA> | <NA> | <NA> | 0.001 | 670.000 | 12.000 | 0.915 | 0.765 | 0.691 | 0.021 | 1.009 | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 299 | 0.733 | 0.708 | 0.758 | RandomForestClassifier() | <NA> | 0.488 | 1.000 | 2.000 | 1.000 | 0.849 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 300 | 0.732 | 0.714 | 0.750 | LogisticRegression() | 0.012 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | CustomOrdinalEncoder() |
| 301 | 0.732 | 0.713 | 0.751 | LinearSVC(random_state=42) | 0.001 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | CustomOrdinalEncoder() |
| 302 | 0.732 | 0.701 | 0.762 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 303 | 0.731 | 0.711 | 0.752 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | CustomOrdinalEncoder() |
| 304 | 0.731 | 0.712 | 0.751 | ExtraTreesClassifier() | <NA> | 0.152 | 41.000 | 47.000 | 49.000 | 0.586 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 305 | 0.731 | 0.708 | 0.753 | ExtraTreesClassifier() | <NA> | 0.837 | 44.000 | 35.000 | 48.000 | 0.875 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 306 | 0.730 | 0.703 | 0.758 | ExtraTreesClassifier() | <NA> | 0.454 | 1.000 | 50.000 | 11.000 | 0.658 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 307 | 0.730 | 0.705 | 0.755 | XGBClassifier() | <NA> | <NA> | 3.000 | <NA> | <NA> | <NA> | <NA> | 0.305 | 233.000 | 27.000 | 0.770 | 0.686 | 0.784 | 0.000 | 4.000 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 308 | 0.730 | 0.713 | 0.747 | RandomForestClassifier() | <NA> | 0.946 | 99.000 | 6.000 | 50.000 | 0.584 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 309 | 0.730 | 0.715 | 0.745 | ExtraTreesClassifier() | <NA> | 0.023 | 12.000 | 13.000 | 37.000 | 0.755 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 310 | 0.730 | 0.710 | 0.749 | LinearSVC(random_state=42) | 0.005 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | CustomOrdinalEncoder() |
| 311 | 0.730 | 0.704 | 0.755 | RandomForestClassifier() | <NA> | 0.950 | 100.000 | 2.000 | 50.000 | 1.000 | gini | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 312 | 0.730 | 0.709 | 0.750 | RandomForestClassifier() | <NA> | 0.950 | 1.000 | 50.000 | 1.000 | 1.000 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 313 | 0.729 | 0.709 | 0.750 | ExtraTreesClassifier() | <NA> | 0.539 | 11.000 | 46.000 | 50.000 | 0.593 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 314 | 0.729 | 0.711 | 0.746 | ExtraTreesClassifier() | <NA> | 0.298 | 63.000 | 25.000 | 48.000 | 0.585 | entropy | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | CustomOrdinalEncoder() |
| 315 | 0.728 | 0.701 | 0.755 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.367 | 214.000 | 1.000 | 0.822 | 0.963 | 0.500 | 0.000 | 4.000 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 316 | 0.728 | 0.697 | 0.759 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 317 | 0.728 | 0.697 | 0.759 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 318 | 0.727 | 0.706 | 0.749 | LogisticRegression() | 0.001 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | CustomOrdinalEncoder() |
| 319 | 0.727 | 0.708 | 0.746 | LinearSVC(random_state=42) | 0.067 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | CustomOrdinalEncoder() |
| 320 | 0.727 | 0.708 | 0.746 | LinearSVC(random_state=42) | 0.069 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | CustomOrdinalEncoder() |
| 321 | 0.726 | 0.709 | 0.744 | LogisticRegression() | 0.361 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | CustomOrdinalEncoder() |
| 322 | 0.726 | 0.702 | 0.751 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 2,000.000 | 20.000 | 0.500 | 1.000 | 0.822 | 0.113 | 2.048 | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 323 | 0.726 | 0.707 | 0.745 | LinearSVC(random_state=42) | 1.043 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | CustomOrdinalEncoder() |
| 324 | 0.726 | 0.708 | 0.744 | XGBClassifier() | <NA> | <NA> | 4.000 | <NA> | <NA> | <NA> | <NA> | 0.039 | 1,585.000 | 4.000 | 1.000 | 0.500 | 0.500 | 0.003 | 3.177 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 325 | 0.726 | 0.707 | 0.745 | LinearSVC(random_state=42) | 0.534 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | CustomOrdinalEncoder() |
| 326 | 0.726 | 0.707 | 0.745 | LinearSVC(random_state=42) | 0.308 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | CustomOrdinalEncoder() |
| 327 | 0.725 | 0.707 | 0.743 | LogisticRegression() | 3.489 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | CustomOrdinalEncoder() |
| 328 | 0.725 | 0.707 | 0.743 | LogisticRegression() | 6.651 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | CustomOrdinalEncoder() |
| 329 | 0.725 | 0.705 | 0.745 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.343 | 457.000 | 1.000 | 0.787 | 0.803 | 1.000 | 1.000 | 3.008 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 330 | 0.725 | 0.707 | 0.743 | LogisticRegression() | 99.939 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | CustomOrdinalEncoder() |
| 331 | 0.725 | 0.707 | 0.743 | LogisticRegression() | 96.676 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | CustomOrdinalEncoder() |
| 332 | 0.725 | 0.704 | 0.746 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.423 | 805.000 | 9.000 | 0.628 | 0.545 | 0.537 | 0.007 | 3.696 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 333 | 0.725 | 0.707 | 0.743 | LogisticRegression() | 10.021 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | CustomOrdinalEncoder() |
| 334 | 0.725 | 0.700 | 0.749 | XGBClassifier() | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | None | OneHotEncoder() |
| 335 | 0.724 | 0.699 | 0.749 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 100.000 | 1.000 | 0.790 | 0.500 | 1.000 | 1.000 | 2.943 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 336 | 0.724 | 0.698 | 0.750 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.261 | 100.000 | 1.000 | 0.500 | 1.000 | 1.000 | 0.000 | 4.000 | SimpleImputer() | None | OneHotEncoder() |
| 337 | 0.724 | 0.702 | 0.746 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.197 | 518.000 | 1.000 | 0.501 | 0.919 | 1.000 | 0.000 | 3.011 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 338 | 0.724 | 0.705 | 0.742 | LogisticRegression() | 0.299 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | CustomOrdinalEncoder() |
| 339 | 0.723 | 0.711 | 0.735 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.500 | 100.000 | 1.000 | 0.500 | 0.500 | 0.623 | 0.000 | 3.511 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 340 | 0.723 | 0.698 | 0.747 | XGBClassifier() | <NA> | <NA> | 8.000 | <NA> | <NA> | <NA> | <NA> | 0.218 | 301.000 | 12.000 | 0.654 | 0.962 | 0.687 | 0.000 | 3.033 | SimpleImputer() | None | OneHotEncoder() |
| 341 | 0.722 | 0.700 | 0.744 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | CustomOrdinalEncoder() |
| 342 | 0.722 | 0.699 | 0.744 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | CustomOrdinalEncoder() |
| 343 | 0.722 | 0.697 | 0.747 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 100.000 | 50.000 | 1.000 | 0.500 | 0.500 | 0.000 | 4.000 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 344 | 0.721 | 0.702 | 0.741 | XGBClassifier() | <NA> | <NA> | 21.000 | <NA> | <NA> | <NA> | <NA> | 0.248 | 591.000 | 2.000 | 0.585 | 0.551 | 0.783 | 0.028 | 3.261 | SimpleImputer() | None | OneHotEncoder() |
| 345 | 0.720 | 0.695 | 0.745 | XGBClassifier() | <NA> | <NA> | 42.000 | <NA> | <NA> | <NA> | <NA> | 0.500 | 110.000 | 23.000 | 0.746 | 0.871 | 0.971 | 1.000 | 4.000 | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 346 | 0.720 | 0.696 | 0.743 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 279.000 | 1.000 | 0.593 | 0.740 | 0.918 | 0.617 | 1.000 | SimpleImputer() | None | OneHotEncoder() |
| 347 | 0.719 | 0.691 | 0.747 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 348 | 0.717 | 0.695 | 0.740 | LinearSVC(random_state=42) | 0.006 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | CustomOrdinalEncoder() |
| 349 | 0.717 | 0.694 | 0.740 | XGBClassifier() | <NA> | <NA> | 2.000 | <NA> | <NA> | <NA> | <NA> | 0.362 | 125.000 | 8.000 | 0.662 | 0.932 | 0.691 | 0.001 | 2.459 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 350 | 0.715 | 0.690 | 0.740 | XGBClassifier() | <NA> | <NA> | 29.000 | <NA> | <NA> | <NA> | <NA> | 0.101 | 758.000 | 4.000 | 0.503 | 0.767 | 0.992 | 0.005 | 3.920 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 351 | 0.713 | 0.685 | 0.741 | LinearSVC(random_state=42) | 98.940 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 352 | 0.710 | 0.688 | 0.733 | LogisticRegression() | 0.012 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | CustomOrdinalEncoder() |
| 353 | 0.709 | 0.682 | 0.737 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 354 | 0.709 | 0.686 | 0.732 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | CustomOrdinalEncoder() |
| 355 | 0.708 | 0.680 | 0.736 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | OneHotEncoder() |
| 356 | 0.704 | 0.681 | 0.728 | XGBClassifier() | <NA> | <NA> | 23.000 | <NA> | <NA> | <NA> | <NA> | 0.467 | 332.000 | 4.000 | 0.844 | 0.895 | 0.705 | 0.080 | 3.455 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 357 | 0.702 | 0.678 | 0.725 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | CustomOrdinalEncoder() |
| 358 | 0.700 | 0.676 | 0.724 | XGBClassifier() | <NA> | <NA> | 3.000 | <NA> | <NA> | <NA> | <NA> | 0.220 | 1,301.000 | 4.000 | 0.667 | 0.830 | 0.878 | 0.522 | 1.162 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 359 | 0.699 | 0.675 | 0.724 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | CustomOrdinalEncoder() |
| 360 | 0.699 | 0.675 | 0.724 | LogisticRegression() | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | CustomOrdinalEncoder() |
| 361 | 0.699 | 0.677 | 0.720 | XGBClassifier() | <NA> | <NA> | 13.000 | <NA> | <NA> | <NA> | <NA> | 0.252 | 655.000 | 4.000 | 0.636 | 0.572 | 0.500 | 0.015 | 1.239 | SimpleImputer() | None | CustomOrdinalEncoder() |
| 362 | 0.695 | 0.663 | 0.727 | XGBClassifier() | <NA> | <NA> | 6.000 | <NA> | <NA> | <NA> | <NA> | 0.490 | 541.000 | 29.000 | 0.506 | 0.558 | 0.747 | 0.006 | 1.012 | SimpleImputer(strategy='most_frequent') | None | CustomOrdinalEncoder() |
| 363 | 0.694 | 0.673 | 0.715 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.000 | 258.000 | 50.000 | 0.921 | 0.605 | 0.882 | 0.000 | 1.356 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 364 | 0.692 | 0.670 | 0.715 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | StandardScaler() | CustomOrdinalEncoder() |
| 365 | 0.690 | 0.670 | 0.710 | XGBClassifier() | <NA> | <NA> | 2.000 | <NA> | <NA> | <NA> | <NA> | 0.488 | 1,174.000 | 7.000 | 0.961 | 0.513 | 0.655 | 0.001 | 1.525 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 366 | 0.690 | 0.634 | 0.745 | LinearSVC(random_state=42) | 12.562 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | CustomOrdinalEncoder() |
| 367 | 0.690 | 0.658 | 0.721 | LinearSVC(random_state=42) | 16.863 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | CustomOrdinalEncoder() |
| 368 | 0.688 | 0.656 | 0.720 | XGBClassifier() | <NA> | <NA> | 3.000 | <NA> | <NA> | <NA> | <NA> | 0.499 | 1,046.000 | 12.000 | 0.531 | 0.932 | 0.824 | 0.084 | 1.067 | SimpleImputer() | None | OneHotEncoder() |
| 369 | 0.683 | 0.662 | 0.704 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | CustomOrdinalEncoder() |
| 370 | 0.679 | 0.652 | 0.707 | LinearSVC(random_state=42) | 7.944 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | StandardScaler() | CustomOrdinalEncoder() |
| 371 | 0.671 | 0.648 | 0.694 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | CustomOrdinalEncoder() |
| 372 | 0.667 | 0.632 | 0.702 | LinearSVC(random_state=42) | 86.192 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | OneHotEncoder() |
| 373 | 0.665 | 0.638 | 0.692 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 374 | 0.664 | 0.642 | 0.686 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | MinMaxScaler() | CustomOrdinalEncoder() |
| 375 | 0.663 | 0.632 | 0.694 | XGBClassifier() | <NA> | <NA> | 50.000 | <NA> | <NA> | <NA> | <NA> | 0.391 | 382.000 | 50.000 | 0.865 | 0.925 | 0.500 | 1.000 | 2.294 | SimpleImputer(strategy='most_frequent') | None | OneHotEncoder() |
| 376 | 0.661 | 0.614 | 0.708 | LinearSVC(random_state=42) | 99.097 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | StandardScaler() | CustomOrdinalEncoder() |
| 377 | 0.659 | 0.628 | 0.689 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.333 | 2,000.000 | 50.000 | 0.969 | 1.000 | 1.000 | 0.206 | 3.019 | SimpleImputer(strategy='median') | None | OneHotEncoder() |
| 378 | 0.653 | 0.606 | 0.701 | XGBClassifier() | <NA> | <NA> | 1.000 | <NA> | <NA> | <NA> | <NA> | 0.169 | 140.000 | 50.000 | 0.680 | 0.639 | 0.811 | 1.000 | 4.000 | SimpleImputer(strategy='median') | None | CustomOrdinalEncoder() |
| 379 | 0.650 | 0.623 | 0.676 | LinearSVC(random_state=42) | 0.000 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 380 | 0.636 | 0.588 | 0.685 | LinearSVC(random_state=42) | 99.775 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer(strategy='median') | MinMaxScaler() | CustomOrdinalEncoder() |
results.to_formatted_dataframe(query='model == "XGBClassifier()"', include_rank=True)
| rank | roc_auc Mean | roc_auc 95CI.LO | roc_auc 95CI.HI | max_depth | learning_rate | n_estimators | min_child_weight | subsample | colsample_bytree | colsample_bylevel | reg_alpha | reg_lambda | imputer | encoder |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.772 | 0.746 | 0.798 | 15.000 | 0.000 | 388.000 | 1.000 | 0.500 | 0.500 | 0.500 | 0.011 | 1.668 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 2 | 0.771 | 0.748 | 0.794 | 50.000 | 0.034 | 100.000 | 1.000 | 0.605 | 0.500 | 0.500 | 0.285 | 4.000 | SimpleImputer(strategy='median') | CustomOrdinalEncoder() |
| 3 | 0.771 | 0.745 | 0.796 | 28.000 | 0.000 | 201.000 | 1.000 | 0.500 | 0.500 | 0.500 | 0.092 | 1.020 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 4 | 0.768 | 0.741 | 0.795 | 50.000 | 0.000 | 228.000 | 1.000 | 0.737 | 0.585 | 1.000 | 0.000 | 1.817 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 5 | 0.767 | 0.744 | 0.791 | 15.000 | 0.038 | 100.000 | 2.000 | 0.500 | 0.500 | 0.808 | 1.000 | 3.491 | SimpleImputer() | CustomOrdinalEncoder() |
| 6 | 0.767 | 0.746 | 0.787 | 50.000 | 0.041 | 100.000 | 1.000 | 0.571 | 0.500 | 0.642 | 1.000 | 1.000 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 7 | 0.766 | 0.743 | 0.790 | 9.000 | 0.000 | 100.000 | 1.000 | 0.708 | 0.500 | 0.500 | 0.015 | 2.079 | SimpleImputer() | CustomOrdinalEncoder() |
| 8 | 0.766 | 0.739 | 0.792 | 50.000 | 0.000 | 2,000.000 | 2.000 | 0.757 | 0.500 | 0.534 | 1.000 | 1.000 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 9 | 0.765 | 0.739 | 0.790 | 16.000 | 0.000 | 373.000 | 2.000 | 0.500 | 0.500 | 0.651 | 1.000 | 1.099 | SimpleImputer(strategy='median') | CustomOrdinalEncoder() |
| 10 | 0.764 | 0.742 | 0.786 | 50.000 | 0.029 | 136.000 | 4.000 | 0.902 | 0.500 | 0.605 | 1.000 | 1.425 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 11 | 0.763 | 0.740 | 0.787 | 1.000 | 0.167 | 100.000 | 1.000 | 0.519 | 1.000 | 0.506 | 1.000 | 1.379 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 12 | 0.763 | 0.743 | 0.783 | 15.000 | 0.037 | 166.000 | 1.000 | 0.500 | 0.500 | 0.500 | 0.306 | 1.526 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 13 | 0.762 | 0.737 | 0.787 | 50.000 | 0.000 | 450.000 | 1.000 | 0.919 | 0.500 | 0.500 | 0.002 | 4.000 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 14 | 0.761 | 0.738 | 0.784 | 36.000 | 0.000 | 100.000 | 1.000 | 0.865 | 0.500 | 0.500 | 1.000 | 3.161 | SimpleImputer() | OneHotEncoder() |
| 15 | 0.760 | 0.737 | 0.783 | 50.000 | 0.000 | 2,000.000 | 3.000 | 0.511 | 0.500 | 0.746 | 1.000 | 1.000 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 16 | 0.760 | 0.735 | 0.785 | 1.000 | 0.110 | 100.000 | 1.000 | 0.679 | 0.500 | 0.500 | 0.000 | 1.605 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 17 | 0.760 | 0.740 | 0.780 | 3.000 | 0.096 | 100.000 | 1.000 | 0.500 | 1.000 | 0.642 | 1.000 | 1.407 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 18 | 0.758 | 0.740 | 0.776 | 10.000 | 0.076 | 100.000 | 4.000 | 0.500 | 0.500 | 0.500 | 0.008 | 3.328 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 19 | 0.757 | 0.733 | 0.782 | 50.000 | 0.000 | 1,368.000 | 4.000 | 0.500 | 0.548 | 0.925 | 0.000 | 1.350 | SimpleImputer() | CustomOrdinalEncoder() |
| 20 | 0.755 | 0.732 | 0.778 | 50.000 | 0.000 | 100.000 | 3.000 | 0.500 | 0.642 | 0.933 | 1.000 | 2.451 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 21 | 0.755 | 0.733 | 0.777 | 50.000 | 0.054 | 183.000 | 5.000 | 0.847 | 0.732 | 0.657 | 0.990 | 2.392 | SimpleImputer() | OneHotEncoder() |
| 22 | 0.754 | 0.731 | 0.778 | 1.000 | 0.242 | 100.000 | 1.000 | 0.838 | 1.000 | 0.500 | 1.000 | 2.956 | SimpleImputer(strategy='median') | CustomOrdinalEncoder() |
| 23 | 0.754 | 0.733 | 0.775 | 1.000 | 0.426 | 100.000 | 1.000 | 0.500 | 0.500 | 0.990 | 0.189 | 2.705 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 24 | 0.754 | 0.732 | 0.776 | 50.000 | 0.000 | 1,698.000 | 5.000 | 0.720 | 0.707 | 0.542 | 0.121 | 1.839 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 25 | 0.753 | 0.730 | 0.776 | 50.000 | 0.042 | 208.000 | 7.000 | 0.679 | 0.500 | 0.595 | 1.000 | 1.749 | SimpleImputer() | CustomOrdinalEncoder() |
| 26 | 0.753 | 0.731 | 0.775 | 1.000 | 0.181 | 100.000 | 1.000 | 0.899 | 1.000 | 1.000 | 0.009 | 3.300 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 27 | 0.753 | 0.728 | 0.778 | 1.000 | 0.101 | 100.000 | 1.000 | 0.798 | 0.500 | 0.500 | 0.053 | 1.470 | SimpleImputer() | OneHotEncoder() |
| 28 | 0.753 | 0.727 | 0.779 | 50.000 | 0.000 | 295.000 | 2.000 | 0.543 | 0.914 | 0.500 | 0.000 | 1.459 | SimpleImputer() | CustomOrdinalEncoder() |
| 29 | 0.753 | 0.727 | 0.778 | 5.000 | 0.004 | 200.000 | 6.000 | 0.867 | 0.625 | 0.614 | 0.221 | 2.698 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 30 | 0.753 | 0.729 | 0.776 | 5.000 | 0.000 | 1,547.000 | 3.000 | 0.976 | 0.629 | 0.993 | 0.000 | 1.000 | SimpleImputer() | OneHotEncoder() |
| 31 | 0.752 | 0.733 | 0.771 | 1.000 | 0.260 | 100.000 | 1.000 | 1.000 | 1.000 | 0.862 | 0.000 | 4.000 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 32 | 0.752 | 0.734 | 0.769 | 50.000 | 0.037 | 100.000 | 4.000 | 1.000 | 0.879 | 0.500 | 0.016 | 1.396 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 33 | 0.750 | 0.732 | 0.767 | 1.000 | 0.500 | 100.000 | 1.000 | 0.528 | 0.730 | 0.781 | 0.007 | 1.149 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 34 | 0.750 | 0.724 | 0.776 | 2.000 | 0.055 | 111.000 | 8.000 | 0.568 | 0.878 | 0.827 | 1.000 | 1.747 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 35 | 0.750 | 0.730 | 0.770 | 4.000 | 0.165 | 100.000 | 1.000 | 1.000 | 0.500 | 0.908 | 1.000 | 3.785 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 36 | 0.746 | 0.722 | 0.770 | 50.000 | 0.000 | 1,499.000 | 2.000 | 1.000 | 0.704 | 1.000 | 0.001 | 1.157 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 37 | 0.745 | 0.719 | 0.772 | 8.000 | 0.062 | 188.000 | 12.000 | 0.735 | 0.852 | 0.798 | 0.108 | 3.989 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 38 | 0.745 | 0.722 | 0.767 | 50.000 | 0.024 | 118.000 | 5.000 | 0.989 | 0.890 | 1.000 | 0.000 | 2.163 | SimpleImputer() | OneHotEncoder() |
| 39 | 0.745 | 0.721 | 0.768 | 36.000 | 0.009 | 2,000.000 | 1.000 | 0.902 | 0.942 | 0.991 | 1.000 | 4.000 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 40 | 0.742 | 0.728 | 0.757 | 1.000 | 0.327 | 228.000 | 1.000 | 0.503 | 0.820 | 1.000 | 0.000 | 1.258 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 41 | 0.741 | 0.719 | 0.763 | 50.000 | 0.102 | 271.000 | 1.000 | 0.917 | 0.661 | 0.500 | 1.000 | 2.473 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 42 | 0.741 | 0.717 | 0.765 | 46.000 | 0.115 | 180.000 | 1.000 | 0.620 | 0.777 | 0.854 | 0.006 | 3.537 | SimpleImputer() | CustomOrdinalEncoder() |
| 43 | 0.740 | 0.715 | 0.766 | 2.000 | 0.000 | 100.000 | 3.000 | 0.669 | 0.774 | 0.798 | 0.004 | 4.000 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 44 | 0.737 | 0.716 | 0.759 | 50.000 | 0.040 | 1,697.000 | 1.000 | 1.000 | 0.500 | 0.500 | 0.001 | 1.095 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 45 | 0.737 | 0.721 | 0.753 | 1.000 | 0.383 | 422.000 | 1.000 | 0.756 | 0.891 | 0.500 | 0.021 | 2.643 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 46 | 0.736 | 0.716 | 0.756 | 1.000 | 0.000 | 100.000 | 13.000 | 0.500 | 0.500 | 0.500 | 0.190 | 2.803 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 47 | 0.735 | 0.713 | 0.757 | 2.000 | 0.000 | 539.000 | 5.000 | 0.513 | 0.881 | 0.997 | 0.000 | 1.000 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 48 | 0.734 | 0.711 | 0.758 | 12.000 | 0.001 | 670.000 | 12.000 | 0.915 | 0.765 | 0.691 | 0.021 | 1.009 | SimpleImputer(strategy='median') | CustomOrdinalEncoder() |
| 49 | 0.730 | 0.705 | 0.755 | 3.000 | 0.305 | 233.000 | 27.000 | 0.770 | 0.686 | 0.784 | 0.000 | 4.000 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 50 | 0.728 | 0.701 | 0.755 | 50.000 | 0.367 | 214.000 | 1.000 | 0.822 | 0.963 | 0.500 | 0.000 | 4.000 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 51 | 0.726 | 0.702 | 0.751 | 50.000 | 0.000 | 2,000.000 | 20.000 | 0.500 | 1.000 | 0.822 | 0.113 | 2.048 | SimpleImputer(strategy='median') | CustomOrdinalEncoder() |
| 52 | 0.726 | 0.708 | 0.744 | 4.000 | 0.039 | 1,585.000 | 4.000 | 1.000 | 0.500 | 0.500 | 0.003 | 3.177 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 53 | 0.725 | 0.705 | 0.745 | 50.000 | 0.343 | 457.000 | 1.000 | 0.787 | 0.803 | 1.000 | 1.000 | 3.008 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 54 | 0.725 | 0.704 | 0.746 | 1.000 | 0.423 | 805.000 | 9.000 | 0.628 | 0.545 | 0.537 | 0.007 | 3.696 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 55 | 0.725 | 0.700 | 0.749 | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | <NA> | SimpleImputer() | OneHotEncoder() |
| 56 | 0.724 | 0.699 | 0.749 | 1.000 | 0.000 | 100.000 | 1.000 | 0.790 | 0.500 | 1.000 | 1.000 | 2.943 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 57 | 0.724 | 0.698 | 0.750 | 50.000 | 0.261 | 100.000 | 1.000 | 0.500 | 1.000 | 1.000 | 0.000 | 4.000 | SimpleImputer() | OneHotEncoder() |
| 58 | 0.724 | 0.702 | 0.746 | 50.000 | 0.197 | 518.000 | 1.000 | 0.501 | 0.919 | 1.000 | 0.000 | 3.011 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 59 | 0.723 | 0.711 | 0.735 | 50.000 | 0.500 | 100.000 | 1.000 | 0.500 | 0.500 | 0.623 | 0.000 | 3.511 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 60 | 0.723 | 0.698 | 0.747 | 8.000 | 0.218 | 301.000 | 12.000 | 0.654 | 0.962 | 0.687 | 0.000 | 3.033 | SimpleImputer() | OneHotEncoder() |
| 61 | 0.722 | 0.697 | 0.747 | 1.000 | 0.000 | 100.000 | 50.000 | 1.000 | 0.500 | 0.500 | 0.000 | 4.000 | SimpleImputer() | CustomOrdinalEncoder() |
| 62 | 0.721 | 0.702 | 0.741 | 21.000 | 0.248 | 591.000 | 2.000 | 0.585 | 0.551 | 0.783 | 0.028 | 3.261 | SimpleImputer() | OneHotEncoder() |
| 63 | 0.720 | 0.695 | 0.745 | 42.000 | 0.500 | 110.000 | 23.000 | 0.746 | 0.871 | 0.971 | 1.000 | 4.000 | SimpleImputer(strategy='median') | CustomOrdinalEncoder() |
| 64 | 0.720 | 0.696 | 0.743 | 1.000 | 0.000 | 279.000 | 1.000 | 0.593 | 0.740 | 0.918 | 0.617 | 1.000 | SimpleImputer() | OneHotEncoder() |
| 65 | 0.717 | 0.694 | 0.740 | 2.000 | 0.362 | 125.000 | 8.000 | 0.662 | 0.932 | 0.691 | 0.001 | 2.459 | SimpleImputer() | CustomOrdinalEncoder() |
| 66 | 0.715 | 0.690 | 0.740 | 29.000 | 0.101 | 758.000 | 4.000 | 0.503 | 0.767 | 0.992 | 0.005 | 3.920 | SimpleImputer() | CustomOrdinalEncoder() |
| 67 | 0.704 | 0.681 | 0.728 | 23.000 | 0.467 | 332.000 | 4.000 | 0.844 | 0.895 | 0.705 | 0.080 | 3.455 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 68 | 0.700 | 0.676 | 0.724 | 3.000 | 0.220 | 1,301.000 | 4.000 | 0.667 | 0.830 | 0.878 | 0.522 | 1.162 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 69 | 0.699 | 0.677 | 0.720 | 13.000 | 0.252 | 655.000 | 4.000 | 0.636 | 0.572 | 0.500 | 0.015 | 1.239 | SimpleImputer() | CustomOrdinalEncoder() |
| 70 | 0.695 | 0.663 | 0.727 | 6.000 | 0.490 | 541.000 | 29.000 | 0.506 | 0.558 | 0.747 | 0.006 | 1.012 | SimpleImputer(strategy='most_frequent') | CustomOrdinalEncoder() |
| 71 | 0.694 | 0.673 | 0.715 | 50.000 | 0.000 | 258.000 | 50.000 | 0.921 | 0.605 | 0.882 | 0.000 | 1.356 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 72 | 0.690 | 0.670 | 0.710 | 2.000 | 0.488 | 1,174.000 | 7.000 | 0.961 | 0.513 | 0.655 | 0.001 | 1.525 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 73 | 0.688 | 0.656 | 0.720 | 3.000 | 0.499 | 1,046.000 | 12.000 | 0.531 | 0.932 | 0.824 | 0.084 | 1.067 | SimpleImputer() | OneHotEncoder() |
| 74 | 0.663 | 0.632 | 0.694 | 50.000 | 0.391 | 382.000 | 50.000 | 0.865 | 0.925 | 0.500 | 1.000 | 2.294 | SimpleImputer(strategy='most_frequent') | OneHotEncoder() |
| 75 | 0.659 | 0.628 | 0.689 | 1.000 | 0.333 | 2,000.000 | 50.000 | 0.969 | 1.000 | 1.000 | 0.206 | 3.019 | SimpleImputer(strategy='median') | OneHotEncoder() |
| 76 | 0.653 | 0.606 | 0.701 | 1.000 | 0.169 | 140.000 | 50.000 | 0.680 | 0.639 | 0.811 | 1.000 | 4.000 | SimpleImputer(strategy='median') | CustomOrdinalEncoder() |
results.to_formatted_dataframe(query='model == "LogisticRegression()"', num_rows=60, include_rank=True)
| rank | roc_auc Mean | roc_auc 95CI.LO | roc_auc 95CI.HI | C | imputer | scaler | encoder |
|---|---|---|---|---|---|---|---|
| 1 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 2 | 0.770 | 0.751 | 0.790 | 0.059 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 3 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 4 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 5 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 6 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 7 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 8 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 9 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 10 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 11 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 12 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 13 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 14 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 15 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 16 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 17 | 0.770 | 0.751 | 0.790 | 0.058 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 18 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 19 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 20 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 21 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 22 | 0.770 | 0.751 | 0.790 | 0.060 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 23 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 24 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 25 | 0.770 | 0.751 | 0.790 | 0.060 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 26 | 0.770 | 0.751 | 0.790 | 0.060 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 27 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 28 | 0.770 | 0.751 | 0.790 | 0.060 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 29 | 0.770 | 0.751 | 0.790 | 0.060 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 30 | 0.770 | 0.751 | 0.790 | 0.061 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 31 | 0.770 | 0.751 | 0.790 | 0.062 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 32 | 0.770 | 0.751 | 0.790 | 0.062 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 33 | 0.770 | 0.751 | 0.790 | 0.062 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 34 | 0.770 | 0.750 | 0.790 | 0.062 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 35 | 0.770 | 0.750 | 0.789 | 0.058 | SimpleImputer(strategy='median') | StandardScaler() | OneHotEncoder() |
| 36 | 0.766 | 0.745 | 0.787 | 0.016 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 37 | 0.765 | 0.744 | 0.785 | 0.281 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 38 | 0.764 | 0.734 | 0.795 | 0.059 | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 39 | 0.764 | 0.735 | 0.793 | 0.132 | SimpleImputer(strategy='median') | MinMaxScaler() | OneHotEncoder() |
| 40 | 0.761 | 0.736 | 0.786 | 0.566 | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 41 | 0.759 | 0.730 | 0.789 | 0.009 | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 42 | 0.759 | 0.737 | 0.781 | 0.008 | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 43 | 0.759 | 0.739 | 0.779 | 0.762 | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 44 | 0.757 | 0.738 | 0.777 | <NA> | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 45 | 0.757 | 0.735 | 0.779 | 1.401 | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 46 | 0.757 | 0.728 | 0.786 | 0.004 | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 47 | 0.756 | 0.727 | 0.784 | 0.001 | SimpleImputer(strategy='median') | MinMaxScaler() | OneHotEncoder() |
| 48 | 0.755 | 0.727 | 0.784 | 0.000 | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 49 | 0.755 | 0.727 | 0.784 | 0.000 | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 50 | 0.755 | 0.727 | 0.784 | 0.000 | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 51 | 0.755 | 0.727 | 0.784 | 0.000 | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 52 | 0.755 | 0.727 | 0.784 | 0.000 | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 53 | 0.751 | 0.732 | 0.770 | 3.854 | SimpleImputer() | MinMaxScaler() | OneHotEncoder() |
| 54 | 0.748 | 0.731 | 0.764 | 8.368 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 55 | 0.745 | 0.728 | 0.762 | 23.327 | SimpleImputer(strategy='median') | MinMaxScaler() | OneHotEncoder() |
| 56 | 0.743 | 0.715 | 0.771 | 0.002 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
| 57 | 0.743 | 0.725 | 0.760 | 98.353 | SimpleImputer(strategy='most_frequent') | MinMaxScaler() | OneHotEncoder() |
| 58 | 0.742 | 0.725 | 0.759 | 88.500 | SimpleImputer() | StandardScaler() | OneHotEncoder() |
| 59 | 0.732 | 0.714 | 0.750 | 0.012 | SimpleImputer() | StandardScaler() | CustomOrdinalEncoder() |
| 60 | 0.732 | 0.701 | 0.762 | 0.000 | SimpleImputer(strategy='most_frequent') | StandardScaler() | OneHotEncoder() |
results.plot_performance_across_trials(facet_by='model').show()
results.plot_performance_across_trials(query='model == "XGBClassifier()"').show()
results.plot_parameter_values_across_trials(query='model == "XGBClassifier()"').show()
# results.plot_scatter_matrix(query='model == "XGBClassifier()"',
# height=1000, width=1000).show()
results.plot_performance_numeric_params(query='model == "XGBClassifier()"',
height=800)
results.plot_parallel_coordinates(query='model == "XGBClassifier()"').show()
results.plot_performance_non_numeric_params(query='model == "XGBClassifier()"').show()
results.plot_score_vs_parameter(
query='model == "XGBClassifier()"',
parameter='learning_rate',
size='colsample_bytree',
color='encoder',
)
results.plot_parameter_vs_parameter(
query='model == "XGBClassifier()"',
parameter_x='colsample_bytree',
parameter_y='learning_rate',
size='max_depth'
)
results.plot_parameter_vs_parameter(
query='model == "XGBClassifier()"',
parameter_x='colsample_bytree',
parameter_y='learning_rate',
size='imputer'
)